Synthesizing larger texture images from a smaller exemplar is an important task in graphics and vision. The conventional CNNs, recently adopted for synthesis, require to train and test on the same set of images and fail to generalize to unseen images. This is mainly because those CNNs fully rely on convolutional and upsampling layers that operate locally and not suitable for a task as global as texture synthesis. In this work, inspired by the repetitive nature of texture patterns, we find that texture synthesis can be viewed as (local) upsampling in the Fast Fourier Transform (FFT) domain. However, FFT of natural images exhibits high dynamic range and lacks local correlations. Therefore, to train CNNs we design a framework to perform FFT upsampling in feature space using deformable convolutions. Such design allows our framework to generalize to unseen images, and synthesize textures in a single pass. Extensive evaluations confirm that our method achieves state-of-the-art performance both quantitatively and qualitatively.

We thank all four reviewers for their great reviews. We provide our feedback for each reviewer as follows. Due to the difficulty of evaluation, we can only use small scale datasets (e.g. CIFAR) to generate figures like Figure 1 (a-c) in our paper to show the correctness. MNIST and CIFAR are standard datasets in influence function or data cleansing literature.

If labels are obtained from elsewhere: documentation discusses where they were obtained from, how they were reused, and how the collected annotations and labels are combined with existing ones. DATA QUALITY 10 Suitability Suitability is a measure of a dataset's Documentation discusses how the dataset Documentation discusses how quality with regards to the purpose is appropriate for the defined purpose.

Data curation is a field with origins in librarianship and archives, whose scholarship and thinking on data issues go back centuries, if not millennia. The field of machine learning is increasingly observing the importance of data curation to the advancement of both applications and fundamental understanding of machine learning models - evidenced not least by the creation of the Datasets and Benchmarks track itself. This work provides an analysis of recent dataset development practices at NeurIPS through the lens of data curation. We present an evaluation framework for dataset documentation, consisting of a rubric and toolkit developed through a thorough literature review of data curation principles. We use the framework to systematically assess the strengths and weaknesses in current dataset development practices of 60 datasets published in the NeurIPS Datasets and Benchmarks track from 2021-2023.

Missing data in multivariate time series are common issues that can affect the analysis and downstream applications. Although multivariate time series data generally consist of the trend, seasonal and residual terms, existing works mainly focus on optimizing the modeling for the first two items. However, we find that the residual term is more crucial for getting accurate fillings, since it is more related to the diverse changes of data and the biggest component of imputation errors. Therefore, in this study, we introduce frequency-domain information and design Frequency-aware Generative Models for Multivariate Time Series Imputation (FGTI). Specifically, FGTI employs a high-frequency filter to boost the residual term imputation, supplemented by a dominant-frequency filter for the trend and seasonal imputation.

In multi-output regression, we identify a previously neglected challenge that arises from the inability of training distribution to cover all combinations of input features, leading to combinatorial distribution shift (CDS). To the best of our knowledge, this is the first work to formally define and address this problem. We tackle it through a novel tensor decomposition perspective, proposing the Functional t-Singular Value Decomposition (Ft-SVD) theorem which extends the classical tensor SVD to infinite and continuous feature domains, providing a natural tool for representing and analyzing multi-output functions. Within the Ft-SVD framework, we formulate the multi-output regression problem under CDS as a low-rank tensor estimation problem under the missing not at random (MNAR) setting, and introduce a series of assumptions about the true functions, training and testing distributions, and spectral properties of the ground-truth embeddings, making the problem more tractable. To address the challenges posed by CDS in multi-output regression, we develop a tailored Double-Stage Empirical Risk Minimization (ERM-DS) algorithm that leverages the spectral properties of the embeddings and uses specific hypothesis classes in each frequency component to better capture the varying spectral decay patterns. We provide rigorous theoretical analyses that establish performance guarantees for the ERM-DS algorithm. This work lays a preliminary theoretical foundation for multi-output regression under CDS.

Multimodal interleaved datasets featuring free-form interleaved sequences of images and text are crucial for training frontier large multimodal models (LMMs). Despite the rapid progression of open-source LMMs, there remains a pronounced scarcity of large-scale, open-source multimodal interleaved datasets. In response, we introduce MINT-1T, the most extensive and diverse open-source Multimodal INTerleaved dataset to date. MINT-1T comprises of one trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. As scaling multimodal interleaved datasets requires substantial engineering effort, sharing the data curation process and releasing the dataset greatly benefits the community. Our experiments show that LMMs trained on MINT-1T rival the performance of models trained on the previous leading dataset, OBELICS.

A.1 Data, Models, and Model Accuracies Images are pixel-wise normalized by the mean and standard deviation of the training images for each dataset, and for ImageNet all images are center cropped and resized to 224 224; this preprocessing is done before any interpolating paths are constructed. Our implementations are based on Cubuk et al. [6]; we use the same optimizer (stochastic gradient descent with momentum) and cosine learning rate schedule. We train without data augmentation (to ensure all models are trained on exactly the same examples), except for experiments that explicitly vary data augmentation. Without data augmentation, the test accuracies of our models are shown in Table 1. The top-1 classification accuracies of these models are presented in Table 2. Model ImageNet Test Accuracy (%) A.2 Linear Interpolation: Methodological Details For each sampled path, we compute the discrete Fourier transform (DFT) of the prediction function along the path separately for each of the M class predictions, take the (real) magnitude of the resulting (complex) DFT coefficients, and average them among the M classes.

Time series forecasting typically needs to address non-stationary data with evolving trend and seasonal patterns. To address the non-stationarity, reversible instance normalization has been recently proposed to alleviate impacts from the trend with certain statistical measures, e.g., mean and variance. Although they demonstrate improved predictive accuracy, they are limited to expressing basic trends and are incapable of handling seasonal patterns. To address this limitation, this paper proposes a new instance normalization solution, called frequency adaptive normalization (FAN), which extends instance normalization in handling both dynamic trend and seasonal patterns. Specifically, we employ the Fourier transform to identify instance-wise predominant frequent components that cover most non-stationary factors. Furthermore, the discrepancy of those frequency components between inputs and outputs is explicitly modeled as a prediction task with a simple MLP model. FAN is a model-agnostic method that can be applied to arbitrary predictive backbones. We instantiate FAN on four widely used forecasting models as the backbone and evaluate their prediction performance improvements on eight benchmark datasets. FAN demonstrates significant performance advancement, achieving 7.76% 37.90% average improvements in MSE.

This supplement to our main paper, "Language Without Borders: A Dataset and Benchmark for Code-Switching Lip Reading," includes detailed descriptions of the dataset collection methods, a comprehensive data card, and datasheets. Additionally, we provide licensing information for the dataset, along with an author statement affirming adherence to the license. Further discussions on the societal impact are included, covering cultural context and privacy considerations. Implementation details of the methods applied to the dataset are also provided. This application, illustrated in Figure 3, not only facilitates the usages of participants, but also ensures the integrity and uniformity of the collected data. Prior to the commencement of the recording, participants are adequately briefed about the entire data collection process and all necessary precautions. This includes detailed instructions for downloading and installing our application, important pre-requisites for successful data collection such as securing a quiet environment for recordings. It guarantees that the participant's face is fully within the video frame and directly facing the camera, and avoiding the presence of additional faces in the recording frame. It is of fundamental importance that during the recording, participants are advised to hold their phone with one hand while maintaining an optimal distance from the camera to achieve clear and properly framed video images. To avoid any distractions or impediments during the recording session, participants are recommended to disable notification alerting from various apps like WeChat or any others that could potentially obstruct the recording interface's prompts.